{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4758a78-8cef-4ba3-9962-c908a1962bf7",
   "metadata": {},
   "source": [
    "# <span style=\"color: #9333EA;\">滑珠图</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48822e6e-9225-4c6c-875a-52e9991a34a7",
   "metadata": {},
   "source": [
    "## 以下是各商品的订单数和目标订单数，用词数据来制作滑珠图"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbb7f75d-8e32-46e4-a05c-229229c9cef7",
   "metadata": {},
   "source": [
    "\r\n",
    "| 商品名称 | 订单数 | 目标订单数 | 差值 |\r\n",
    "| --- | --- | --- | --- |\r\n",
    "| 裙子 | 120 | 118 | -2 |\r\n",
    "| 毛衣 | 107 | 119 | 12 |\r\n",
    "| 卫衣 | 127 | 142 | 15 |\r\n",
    "| 裤子 | 134 | 128 | -6 |\r\n",
    "| 夹克 | 126 | 125 | -1 |\r\n",
    "| 外套 | 123 | 127 | 4 |\r\n",
    "| T恤 | 134 | 132 | -2 |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03f5f1bf-f078-47ca-a173-819506e44946",
   "metadata": {},
   "source": [
    "# 滑珠图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b97fdd3-3492-4736-a28e-a5fdbcaf8582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "完成率数据：\n",
      "  商品名称  订单数  目标订单数     完成率\n",
      "0   裙子  120    118  101.69\n",
      "1   毛衣  107    119   89.92\n",
      "2   卫衣  127    142   89.44\n",
      "3   裤子  134    128  104.69\n",
      "4   夹克  126    125  100.80\n",
      "5   外套  123    127   96.85\n",
      "6   T恤  134    132  101.52\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import FancyBboxPatch, Circle\n",
    "\n",
    "# 使用之前计算的完成率数据\n",
    "data = {\n",
    "    '商品名称': ['裙子', '毛衣', '卫衣', '裤子', '夹克', '外套', 'T恤'],\n",
    "    '订单数': [120, 107, 127, 134, 126, 123, 134],\n",
    "    '目标订单数': [118, 119, 142, 128, 125, 127, 132],\n",
    "    '完成率': [101.69, 89.92, 89.44, 104.69, 100.80, 96.85, 101.52]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(\"完成率数据：\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96f8fe0f-077e-4e26-ba88-79a2bc3b23a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.patches import Circle\n",
    "\n",
    "# 使用之前计算的完成率数据\n",
    "data = {\n",
    "    '商品名称': ['裙子', '毛衣', '卫衣', '裤子', '夹克', '外套', 'T恤'],\n",
    "    '订单数': [120, 107, 127, 134, 126, 123, 134],\n",
    "    '目标订单数': [118, 119, 142, 128, 125, 127, 132],\n",
    "    '完成率': [101.69, 89.92, 89.44, 104.69, 100.80, 96.85, 101.52]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# 创建滑珠图\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "\n",
    "# 设置深蓝色背景\n",
    "fig.patch.set_facecolor('#1a1a3b')\n",
    "ax.set_facecolor('#1a1a3b')\n",
    "\n",
    "# 设置图表标题\n",
    "ax.text(0.5, 0.95, '2024年商品订单目标达成率情况', \n",
    "        transform=ax.transAxes, ha='center', va='top', \n",
    "        fontsize=18, color='white', fontweight='bold')\n",
    "\n",
    "# 找出最高和最低完成率的商品\n",
    "max_product = df.loc[df['完成率'].idxmax(), '商品名称']\n",
    "max_rate = df['完成率'].max()\n",
    "min_product = df.loc[df['完成率'].idxmin(), '商品名称']\n",
    "min_rate = df['完成率'].min()\n",
    "\n",
    "# 添加注释文字\n",
    "ax.text(0.5, 0.89, f'{max_product}完成率最高达到{max_rate:.0f}%，{min_product}最低{min_rate:.0f}%', \n",
    "        transform=ax.transAxes, ha='center', va='top', \n",
    "        fontsize=12, color='#4ECDC4')\n",
    "\n",
    "# 设置y轴位置（从上到下排列商品）\n",
    "y_positions = np.arange(len(df))-1\n",
    "\n",
    "# 绘制背景条（灰色）\n",
    "bar_height = 0.8\n",
    "ax.barh(y_positions, [120]*len(df), height=bar_height, color='gray', alpha=0.5, left=0)\n",
    "\n",
    "# 绘制完成率条（蓝色）\n",
    "colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FECA57', '#FF9FF3', '#A8E6CF']\n",
    "for i, (pos, rate) in enumerate(zip(y_positions, df['完成率'])):\n",
    "    # 限制完成率最大值为120%以适应图表\n",
    "    display_rate = min(rate, 120)\n",
    "    ax.barh(pos, display_rate, height=bar_height, color=colors[i], alpha=0.8, left=0)\n",
    "    \n",
    "    # 添加商品名称\n",
    "    ax.text(-5, pos, df.iloc[i]['商品名称'], ha='left', va='center', \n",
    "            fontsize=12, color='white')\n",
    "    \n",
    "    # 添加完成率数值和圆点\n",
    "    ax.text(display_rate + 2, pos, f'{rate:.1f}%', ha='left', va='center', \n",
    "            fontsize=11, color='white', fontweight='bold')\n",
    "    \n",
    "    # 添加蓝色小圆圈\n",
    "    circle = Circle((display_rate, pos), radius=0.3, color=colors[i], alpha=0.8)\n",
    "    ax.add_patch(circle)\n",
    "\n",
    "# 设置坐标轴\n",
    "ax.set_xlim(0, 120)\n",
    "ax.set_ylim(-0.5, len(df)-0.5)\n",
    "ax.set_xticks(range(0, 121, 20))\n",
    "ax.set_xticklabels([f'{x}%' for x in range(0, 121, 20)], color='white')\n",
    "ax.set_yticks([])  # 隐藏y轴刻度\n",
    "\n",
    "# 设置网格线\n",
    "ax.grid(True, axis='x', alpha=0.2, color='white')\n",
    "\n",
    "# 设置坐标轴颜色\n",
    "ax.spines['bottom'].set_color('white')\n",
    "ax.spines['top'].set_color('white')\n",
    "ax.spines['right'].set_color('white')\n",
    "ax.spines['left'].set_color('white')\n",
    "ax.tick_params(axis='x', colors='white')\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5840bdd3-7e08-4599-83d5-c93a57ff389f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
